1. Field of the Invention
The present invention relates to a system processing digital images with a lot of background noises, such as latent fingerprint images and the like, using a computer.
2. Description of the Related Art
Generally, a fingerprint configured with a plurality of streak pattern ridgelines has two main characteristics, permanence and uniqueness. Therefore, fingerprints have been used in criminal investigations from old times. In particular, collation using the latent fingerprints left behind in criminal scenes is effective as a way to help the investigations. Recently, a fingerprint matching system using computers has been introduced, and latent fingerprint matching is conducted in various police agencies.
However, many of the images of the latent fingerprints are of low quality with a noise, which makes it difficult for an investigator to make a judgment. This is also a large factor for hindering the system from being automated. There are many kinds of background noises in the latent fingerprints. One of those is a background noise with irregular shape represented by letters (hereinafter, such a noise is referred to as a “character noise”). FIG. 4A illustrates an example of a latent fingerprint. As in the example, there are cases where fingerprint ridgelines are left on characters and ruled lines of checks. Such character noises are likely to be misjudged and extracted as the fingerprint ridgelines with a related art, so that it is difficult to enhance or extract only the fingerprint ridgelines.
As a related technique for eliminating the background pattern noise, it is common to employ Fourier transformation.
However, when this technique is employed for eliminating the character noises from a fingerprint image, it is necessary for the character noises to appear periodically. Thus, the effect thereof is limited. Further, when the periodicity of the character noises is similar to the periodicity of the fingerprint ridgelines, the fingerprint ridgelines are eliminated as well. Accordingly, the effect is limited. Furthermore, the density of the fingerprint ridgelines in the area with no character noise is deteriorated with the character noise eliminating processing, so that the effect thereof is also limited.
Further, there are various measures proposed as a related method for enhancing the fingerprint ridgelines, in which the direction and periodicity of local ridgelines are extracted, and the ridgelines are enhanced through filter processing that corresponds to the extracted direction and periodicity. This method is proposed in “Fingerprint Image Enhancement: Algorithm and Performance Evaluation (1998)” by Hong, et al., IEEE Transactions on Pattern Analysis and Machine Intelligence (Non-patent Document 2) and Japanese Unexamined Patent Publication 2002-99912 (Patent Document 1).
However, these related arts are not effective when the ridgeline directions and periodicities cannot be extracted properly due to the influence of the character noise. Thus, the issue still remains to be overcome.
As the character noise eliminating technique, there is an effective method proposed by the present inventor in a Japanese Patent Application 2006-239554. This invention includes: a character noise area detection device for detecting a character noise area which corresponds to a character noise from an image; a density conversion area layer determination device for setting a plurality of density conversion area layer inside and outside the character noise area; and a density conversion device for, as a reference area for a target pixel, with respect to the pixels included in the density conversion area layer, setting a neighboring pixel group within the same density conversion area layer as the density conversion area layer to which the target pixel belongs, and generating a density converted image by applying a local image enhancement. Thus, the character noise can be eliminated effectively by automated processing.
However, with the method described in a specification of Japanese Patent Application 2006-239554, character noise areas can be eliminated only in a case where minimum densities of the character noise areas are higher than a maximum density of fingerprint ridgelines. If the density of the fingerprint ridgelines area is higher than the density of the character noise area, the character noise area cannot be extracted completely. Consequently, the character noise areas cannot be fully eliminated. Further, in the character noise area detecting processing, a fingerprint ridgeline area may be misjudged as a character noise area and extracted, that ends up eliminating a fingerprint ridgeline. That is an adverse effect.